AI Supply Chain Optimization Expert

2 weeks ago

Jaipur, Rajasthan, India beBeeSpecialist Full time ₹ 21,69,000 - ₹ 24,39,500

Job Summary:

We seek a Supply Chain AI Specialist to optimize the end-to-end automotive manufacturing and logistics processes by customizing, deploying, and maintaining autonomous AI systems.

Key Responsibilities
  • Collaborate with OEMs, Tier 1 suppliers, logistics providers, and IT teams to understand unique supply chain workflows.
  • Design AI-driven autonomous agents for specific automotive operations such as:
  • Just-in-time (JIT) parts procurement.
  • Dynamic inventory management for critical components.
  • Supplier negotiation agents for procurement contracts.
  • Routing and logistics optimization for vehicle assembly parts.
  • Real-time production scheduling adjustments.
  • Tailor data integration with automotive ERP (e.g., SAP, Oracle), Manufacturing Execution Systems (MES), and IoT sensors on production lines and logistics assets.
  • Deploy predictive models for demand forecasting of automotive components.
  • Implement autonomous negotiation agents that interact with suppliers and logistics providers.
  • Develop multi-agent systems for managing interdepartmental tasks (production, quality control, procurement).
  • Build custom APIs and middleware for automotive-specific platforms:
  • Vehicle assembly planning systems.
  • Supplier portals.
  • Fleet management for logistics.
  • IoT systems monitoring production lines and warehouse conditions.
  • Enable seamless communication between AI agents and legacy systems in the automotive environment.
  • Implement workflows for:
  • Automated inventory replenishment based on predictive analytics.
  • Dynamic rerouting of logistics for vehicle parts during disruptions.
  • Autonomous quality inspection alerts and actions.
  • Supplier engagement and negotiation in response to market fluctuations.
  • Conduct simulations reflecting automotive supply chain scenarios:
  • Component shortages.
  • Logistic delays.
  • Production line adjustments.
  • Validate AI agent decisions against automotive KPIs like throughput, downtime, supplier lead times, and inventory costs.
  • Manage staged deployment in automotive production environments.
  • Monitor system and agent performance, including decision accuracy and response times.
  • Tweak models and agent behaviors based on real-world feedback and evolving automotive market demands.
  • Document integrations, workflows, and agent behaviors in automotive-specific contexts.
  • Train supply chain teams, logistics personnel, and manufacturing staff on interacting with autonomous systems.
  • Provide ongoing support and iterative improvements.

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